{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c6df081a-3dae-4fdc-b9be-85ed37b47b57",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-06T10:24:08.314777Z",
     "start_time": "2024-10-06T10:24:04.931935Z"
    }
   },
   "outputs": [],
   "source": [
    "#数据操作\n",
    "import math\n",
    "import numpy as np\n",
    "#读取、写入数据\n",
    "import pandas as pd\n",
    "import os\n",
    "import csv\n",
    "#可视化进度，进度条\n",
    "from tqdm import tqdm\n",
    "#Pytorch\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import DataLoader,Dataset,random_split\n",
    "#绘制图像\n",
    "from torch.utils.tensorboard import SummaryWriter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ebcac4c-fb47-45ed-a452-6e3c4e4ae1ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-06T10:24:08.320470Z",
     "start_time": "2024-10-06T10:24:08.315774Z"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"一些操作\"\"\"\n",
    "#设置随机种子。复现试验结果，当做模板来用\n",
    "#CUDA的默认操作\n",
    "def same_seed(seed):\n",
    "    torch.backends.cudnn.deterministic =True\n",
    "    torch.backends.cudnn.benchmark =False\n",
    "    np.random.seed(seed)\n",
    "    #用cpu训练模型，为cpu设置随机种子\n",
    "    torch.manual_seed(seed)\n",
    "    if torch.cuda.is_available():\n",
    "        torch.cuda.manual_seed_all(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "488f3947-8c5f-46b8-94d3-ade3827d044e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-06T10:24:08.330793Z",
     "start_time": "2024-10-06T10:24:08.321464Z"
    }
   },
   "outputs": [],
   "source": [
    " #划分数据集\n",
    "def train_valid_split(data_set,valid_ratio,seed):\n",
    "    valid_data_size=int(len(data_set)*valid_ratio)\n",
    "    train_data_size=len(data_set)-valid_data_size\n",
    "    train_data,valid_data=random_split(data_set,[train_data_size,valid_data_size]\n",
    "        ,generator=torch.Generator().manual_seed(seed))\n",
    "    return np.array(train_data),np.array(valid_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b8809dc8-6478-46f0-90d1-4d816593af42",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-06T10:24:08.339556Z",
     "start_time": "2024-10-06T10:24:08.331795Z"
    }
   },
   "outputs": [],
   "source": [
    "#选择特征。select_all是缺省参数，默认117个特征全选\n",
    "def select_feat(train_data,valid_data,test_data,select_all=True):\n",
    "    y_train=train_data[:,-1]#行全部，列最后一列  \n",
    "    y_valid=valid_data[:,-1]\n",
    "\n",
    "    raw_x_train=train_data[:,:-1]\n",
    "    raw_x_valid=valid_data[:,:-1] \n",
    "\n",
    "    raw_x_test=test_data \n",
    "    if select_all:#如果select_all是False\n",
    "        feat_idx=list(range(raw_x_train.shape[1]))\n",
    "    else:\n",
    "        feat_idx=[0,1,2,3,4]#随便写的\n",
    "    return raw_x_train[:,feat_idx],raw_x_valid[:,feat_idx],raw_x_test[:,feat_idx],y_train,y_valid\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7581eb7f-1176-4c7f-9469-c136cdef717f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-06T10:24:08.348482Z",
     "start_time": "2024-10-06T10:24:08.341558Z"
    }
   },
   "outputs": [],
   "source": [
    "#数据集\n",
    "#targets在缺省的情况下，默认值是空值。为None时做的是预测\n",
    "class COVID19Dataset(Dataset):\n",
    "    def __init__(self,features,targets=None):\n",
    "        if targets is None: #target就是标签\n",
    "            self.targets=targets\n",
    "        else:\n",
    "            self.targets=torch.FloatTensor(targets)\n",
    "        #无论是训练还是预测，都需要特征\n",
    "        self.features=torch.FloatTensor(features)#转化为浮点型张量形式\n",
    "            \n",
    "    def __getitem__(self,idx):\n",
    "        if self.targets is None:#测试只用到特征\n",
    "            return self.features[idx]\n",
    "        else:#做训练，返回特征和标签\n",
    "            return self.features[idx],self.targets[idx]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4dcc8049-825e-4eac-bf17-33c4a5b476a3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-06T10:24:08.357544Z",
     "start_time": "2024-10-06T10:24:08.349484Z"
    }
   },
   "outputs": [],
   "source": [
    "#神经网络\n",
    "class My_Model(nn.Module):\n",
    "    def __init__(self,input_dim):#input_dim是输入数据的维度\n",
    "        super(My_Model,self).__init__()\n",
    "        self.layers=nn.Sequential(\n",
    "            nn.Linear(input_dim,16),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(16,8),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(8,1)\n",
    "        )\n",
    "\n",
    "    def forward(self,x):\n",
    "        x=self.layers(x)\n",
    "        x=x.squeeze(1)#删除大小为1的维度\n",
    "        return x\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7c2c8ab8-3a8b-4558-b5ad-cfbc8a447c29",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-06T10:24:08.366471Z",
     "start_time": "2024-10-06T10:24:08.358547Z"
    }
   },
   "outputs": [],
   "source": [
    "#参数设置\n",
    "device=\"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "config={\n",
    "    'seed':5201314,\n",
    "    'select_all':True,\n",
    "    'valid_ratio':0.2,\n",
    "    'n_epochs':3000,#训练趟数\n",
    "    'batch_size':256,\n",
    "    'learning_rate':1e-5,\n",
    "    'early_stop':400,#执行了400次model没有更新，不用训练完3000趟\n",
    "    'save_path':'./models/model.ckpt'#模型保存路径\n",
    "}\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "db34c6a1-ece3-4cd2-af3d-38a0b0d886b8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-06T10:24:08.377064Z",
     "start_time": "2024-10-06T10:24:08.367519Z"
    }
   },
   "outputs": [],
   "source": [
    "#训练过程\n",
    "def trainer(train_loader,valid_loader,model,config,device):\n",
    "    criterion=nn.MSELoss(reduction=\"mean\")\n",
    "    optimizer=torch.optim.SGD(model.parameters(),lr=config['learning_rate'],momentum=0.9)\n",
    "    writer=SummaryWriter()\n",
    "    if not os.path.isdir('./models'):\n",
    "        os.mkdir('./models')#开创文件夹，存储模型\n",
    "\n",
    "    n_epochs=config['n_epochs']\n",
    "    best_loss=math.inf#初始值为无穷大\n",
    "    step=0#纪录训练多少笔数据\n",
    "    early_step_count=0#验证时模型没有改变就+1，发生改变重新清零，就是达到400趟没有变可以提前终止\n",
    "\n",
    "    for epoch in range(n_epochs):\n",
    "        model.train()\n",
    "        loss_record=[]\n",
    "        #进度条，相当于给dataloader重新封装加了几个功能\n",
    "        train_pbar=tqdm(train_loader,position=0,leave=True)\n",
    "\n",
    "        #train loop\n",
    "        for x,y in train_pbar:\n",
    "            optimizer.zero_grad()\n",
    "            x,y=x.to(device),y.to(device)#挪到训练平台 \n",
    "            pred=model(x)\n",
    "            loss=criterion(pred,y)\n",
    "            loss.backward()#计算梯度\n",
    "            optimizer.step()#更新模型\n",
    "            step +=1\n",
    "            loss_record.append(loss.detach().item())\n",
    "            #显示训练过程\n",
    "            train_pbar.set_description(f'Epoch[{epoch+1}/{n_epochs}]')\n",
    "            train_pbar.set_postfix({'loss':loss.detach().item()})\n",
    "\n",
    "        mean_train_loss=sum(loss_record)/len(loss_record)\n",
    "        writer.add_scalar('Loss/train', mean_train_loss,step)\n",
    "\n",
    "        #valid loop\n",
    "        loss_record=[]\n",
    "\n",
    "        for x,y in valid_loader:\n",
    "            x,y=x.to(device),y.to(device)\n",
    "            with torch.no_grad():\n",
    "                pred=model(x)\n",
    "                loss=criterion(pred,y)\n",
    "            loss_record.append(loss.item()) \n",
    "        mean_valid_loss=sum(loss_record)/len(loss_record)\n",
    "        print(f\"Epoch[{epoch+1}/{n_epochs}],Train loss:{mean_train_loss:.4f},Valid loss:{mean_valid_loss:.4f}\")\n",
    "        writer.add_scalar(\"Loss/Valid\",mean_valid_loss,step)\n",
    "\n",
    "        if mean_valid_loss<best_loss:\n",
    "            best_loss=mean_valid_loss\n",
    "            torch.save(model.state_dict(),config['save_path'])#保存新宝兴\n",
    "\n",
    "            print('Saving model with loss{:.3f}...',format(best_loss))\n",
    "            early_stop_count=0\n",
    "        else:\n",
    "            early_stop_count +=1\n",
    "\n",
    "        if early_stop_count>=config['early_stop']:\n",
    "            print('\\n Model is not improving,so we halt the training session')\n",
    "            return\n",
    "            \n",
    "             \n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eaaca28a-326f-46c6-9c74-553826baa7bf",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-10-06T10:24:08.378067Z"
    },
    "jupyter": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_data_size:(2160, 95),valid_data_size:(540, 95),test_data_size:(893, 94)\n",
      "the number of features:94\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[1/3000]: 100%|██████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 179.96it/s, loss=133]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[1/3000],Train loss:221.9564,Valid loss:199.7766\n",
      "Saving model with loss{:.3f}... 199.7766024271647\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[2/3000]: 100%|█████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 209.26it/s, loss=90.1]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[2/3000],Train loss:98.2000,Valid loss:100.8277\n",
      "Saving model with loss{:.3f}... 100.82769203186035\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[3/3000]: 100%|█████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 219.46it/s, loss=69.7]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[3/3000],Train loss:73.2249,Valid loss:69.7711\n",
      "Saving model with loss{:.3f}... 69.7710952758789\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[4/3000]: 100%|█████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 243.19it/s, loss=49.9]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/3000],Train loss:54.8660,Valid loss:58.6444\n",
      "Saving model with loss{:.3f}... 58.64437961578369\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[5/3000]: 100%|█████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 219.46it/s, loss=43.5]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[5/3000],Train loss:47.8266,Valid loss:47.4360\n",
      "Saving model with loss{:.3f}... 47.435971895853676\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[6/3000]: 100%|█████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 230.71it/s, loss=43.8]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[6/3000],Train loss:43.5709,Valid loss:43.8159\n",
      "Saving model with loss{:.3f}... 43.81592973073324\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[7/3000]: 100%|█████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 243.19it/s, loss=51.5]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[7/3000],Train loss:45.4030,Valid loss:48.0505\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[8/3000]: 100%|█████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 230.72it/s, loss=53.8]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[8/3000],Train loss:43.8126,Valid loss:44.0442\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[9/3000]: 100%|█████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 236.79it/s, loss=36.7]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[9/3000],Train loss:38.8711,Valid loss:41.0909\n",
      "Saving model with loss{:.3f}... 41.09093221028646\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[10/3000]: 100%|████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 209.26it/s, loss=39.7]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[10/3000],Train loss:39.8201,Valid loss:40.6296\n",
      "Saving model with loss{:.3f}... 40.6295862197876\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[11/3000]: 100%|████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 209.25it/s, loss=36.2]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[11/3000],Train loss:36.4671,Valid loss:37.7422\n",
      "Saving model with loss{:.3f}... 37.74217208226522\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[12/3000]: 100%|████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 214.24it/s, loss=36.4]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[12/3000],Train loss:34.4049,Valid loss:35.5109\n",
      "Saving model with loss{:.3f}... 35.51093943913778\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[13/3000]: 100%|████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 230.72it/s, loss=30.1]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[13/3000],Train loss:31.5275,Valid loss:32.2954\n",
      "Saving model with loss{:.3f}... 32.29540570576986\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[14/3000]: 100%|████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 219.46it/s, loss=26.1]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[14/3000],Train loss:28.8395,Valid loss:29.3043\n",
      "Saving model with loss{:.3f}... 29.304295857747395\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[15/3000]: 100%|████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 236.79it/s, loss=38.5]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[15/3000],Train loss:27.3683,Valid loss:33.3078\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[16/3000]: 100%|████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 224.98it/s, loss=33.2]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[16/3000],Train loss:35.6953,Valid loss:36.1404\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[17/3000]: 100%|████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 249.94it/s, loss=40.7]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[17/3000],Train loss:26.4212,Valid loss:28.8807\n",
      "Saving model with loss{:.3f}... 28.880719979604084\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[18/3000]: 100%|████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 230.72it/s, loss=50.1]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[18/3000],Train loss:51.2252,Valid loss:50.2305\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[19/3000]: 100%|████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 236.79it/s, loss=52.3]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[19/3000],Train loss:42.0058,Valid loss:41.2753\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[20/3000]: 100%|████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 219.47it/s, loss=36.9]\n"
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     "text": [
      "Epoch[20/3000],Train loss:35.2268,Valid loss:34.7154\n"
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      "Epoch[21/3000],Train loss:27.3707,Valid loss:26.8158\n",
      "Saving model with loss{:.3f}... 26.815840244293213\n"
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      "Epoch[22/3000],Train loss:20.8166,Valid loss:20.7424\n",
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      "Epoch[23/3000],Train loss:15.7514,Valid loss:16.1789\n",
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      "Epoch[24/3000],Train loss:20.6883,Valid loss:22.6209\n"
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      "Epoch[25/3000],Train loss:43.9349,Valid loss:51.2701\n"
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      "Epoch[26/3000],Train loss:83.1854,Valid loss:100.7021\n"
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      "Epoch[27/3000],Train loss:78.7309,Valid loss:78.6770\n"
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      "Epoch[28/3000],Train loss:56.3750,Valid loss:56.4761\n"
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      "Epoch[29/3000],Train loss:45.4703,Valid loss:45.6021\n"
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      "Epoch[30/3000],Train loss:31.6701,Valid loss:33.1979\n"
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      "Epoch[66/3000],Train loss:8.0329,Valid loss:8.5325\n",
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      "Epoch[247/3000],Train loss:3.7077,Valid loss:3.6902\n",
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      "Epoch[380/3000],Train loss:9.8507,Valid loss:11.6479\n"
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      "Epoch[472/3000],Train loss:2.6199,Valid loss:2.5566\n"
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      "Epoch[474/3000],Train loss:2.0712,Valid loss:1.9935\n",
      "Saving model with loss{:.3f}... 1.9934963633616765\n"
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      "Epoch[2543/3000]: 100%|██████████████████████████████████████████████████████| 9/9 [00:00<00:00, 199.84it/s, loss=1.76]\n"
     ]
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     "text": [
      "Epoch[2543/3000],Train loss:1.7744,Valid loss:1.6509\n"
     ]
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     "output_type": "stream",
     "text": [
      "Epoch[2544/3000]: 100%|██████████████████████████████████████████████████████| 9/9 [00:00<00:00, 196.42it/s, loss=1.64]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[2544/3000],Train loss:1.7375,Valid loss:2.0221\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch[2545/3000]: 100%|██████████████████████████████████████████████████████| 9/9 [00:00<00:00, 186.24it/s, loss=1.55]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[2545/3000],Train loss:1.5651,Valid loss:1.6125\n"
     ]
    },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[2546/3000]: 100%|██████████████████████████████████████████████████████| 9/9 [00:00<00:00, 192.49it/s, loss=1.36]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[2546/3000],Train loss:1.2525,Valid loss:1.4861\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[2547/3000]: 100%|███████████████████████████████████████████████████████| 9/9 [00:00<00:00, 212.19it/s, loss=1.3]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[2547/3000],Train loss:1.2232,Valid loss:1.3179\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[2548/3000]: 100%|██████████████████████████████████████████████████████| 9/9 [00:00<00:00, 210.96it/s, loss=1.48]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[2548/3000],Train loss:1.2267,Valid loss:1.2479\n"
     ]
    },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch[2549/3000]:   0%|                                                                | 0/9 [00:00<?, ?it/s, loss=1.2]IOPub message rate exceeded.\n",
      "The Jupyter server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--ServerApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
      "\n"
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   ],
   "source": [
    "#准备工作\n",
    "#设置随机种子\n",
    "same_seed(config['seed'])#放GPU上打开\n",
    "#读取数据\n",
    "train_data=pd.read_csv('./covid.train.csv').values#一个点表示当前工作目录下\n",
    "test_data=pd.read_csv('./covid.test.csv').values\n",
    "#划分数据集\n",
    "train_data,valid_data=train_valid_split(train_data,config['valid_ratio'],config['seed'])\n",
    "print(f\"train_data_size:{train_data.shape},valid_data_size:{valid_data.shape},test_data_size:{test_data.shape}\")\n",
    "#选择特征\n",
    "x_train,x_valid,x_test,y_train,y_valid=select_feat(train_data,valid_data,test_data,config['select_all'])\n",
    "print(f\"the number of features:{x_train.shape[1]}\")#1表示取列数\n",
    "#构造数据集\n",
    "train_dataset=COVID19Dataset(x_train,y_train)\n",
    "valid_dataset=COVID19Dataset(x_valid,y_valid)\n",
    "test_dataset=COVID19Dataset(x_test)\n",
    "#准备Dataloader\n",
    "train_loader=DataLoader(train_dataset,batch_size=config['batch_size'],shuffle=True,pin_memory=True)\n",
    "valid_loader=DataLoader(valid_dataset,batch_size=config['batch_size'],shuffle=True,pin_memory=True)\n",
    "test_loader=DataLoader(test_dataset,batch_size=config['batch_size'],shuffle=False,pin_memory=True)\n",
    "#开始训练\n",
    "model=My_Model(input_dim=x_train.shape[1]).to(device)\n",
    "trainer(train_loader,valid_loader,model,config,device)"
   ]
  },
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   "execution_count": 11,
   "id": "2bbb5b02-ec48-4443-b04b-668f4d9e8796",
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     "name": "stderr",
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     "text": [
      "C:\\Users\\ZTC\\AppData\\Local\\Temp\\ipykernel_14816\\2227419424.py:20: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  model.load_state_dict(torch.load(config['save_path']))\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 799.75it/s]\n"
     ]
    }
   ],
   "source": [
    "#预测\n",
    "def predict(test_loader,model,device):\n",
    "    model.eval()\n",
    "    preds=[]\n",
    "    for x in tqdm(test_loader):\n",
    "        x=x.to(device)\n",
    "        with torch.no_grad():\n",
    "            pred=model(x)\n",
    "            preds.append(pred.detach().cpu())#移动cpu上，方便转换成numpy数组形式打印\n",
    "    preds=torch.cat(preds,dim=0).numpy()\n",
    "    return preds \n",
    "def save_pred(preds,file):\n",
    "    with open(file,'w') as fp:\n",
    "        writer=csv.writer(fp)#创建一个csv写入器对象\n",
    "        writer.writerow(['id','tested_positive'])#写入csv文件的表头\n",
    "        for i,p in enumerate(preds):\n",
    "            writer.writerow([i,p])\n",
    "#预测并保存结果\n",
    "model=My_Model(input_dim=x_train.shape[1]).to(device)\n",
    "model.load_state_dict(torch.load(config['save_path']))\n",
    "preds=predict(test_loader,model,device)\n",
    "save_pred(preds,'pred.csv')\n",
    "        "
   ]
  },
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